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2.
Western Pac Surveill Response J ; 13(2): 1-7, 2022.
Article in English | MEDLINE | ID: covidwho-1994389

ABSTRACT

Objective: Verbal autopsy (VA) through face-to-face interviews with caregivers is a way to determine cause of death without medical certification. In Malaysia, the use of VA has improved mortality statistics. However, during the coronavirus disease 2019 (COVID-19) pandemic, face-to-face interviews were delayed, reducing VA data collection and affecting data for mortality surveillance. This study aims to investigate the feasibility and acceptability of conducting VA interviews via telephone calls, and the quality of the data gathered. Methods: The study was conducted in Malaysia from September to October 2020 using a cross-sectional design. Participants were health-care workers from established VA teams across the country. They conducted VA interviews via telephone and provided feedback through a customized online form. Data collected from the form were used to assess the feasibility, acceptability and quality of the telephone interviews using IBM SPSS version 23. Results: Responses were received from 113 participants. There were 74 (65.5%) successful interviews, representing 91% of the 81 cases who were able to be contacted. More than two thirds of health-care workers provided positive feedback on the telephone interview method for themselves and the interviewees. Only 10.8% of causes of death were unusable. Discussion: This study provides preliminary evidence that VA via telephone interview is feasible, acceptable and can be used as an alternative to face-to-face interviews without affecting data quality. During times when face-to-face interviews are not advisable, VA telephone interviews can be used for data collection for mortality surveillance.


Subject(s)
COVID-19 , Pandemics , Humans , Autopsy/methods , Feasibility Studies , Cause of Death , Cross-Sectional Studies , Malaysia/epidemiology , Telephone
3.
Western Pac Surveill Response J ; 12(1): 46-52, 2021.
Article in English | MEDLINE | ID: covidwho-1259710

ABSTRACT

BACKGROUND: Pre-existing comorbidities can predict severe disease requiring intensive care unit (ICU) admission among COVID-19 cases. We compared comorbidities, clinical features and other predictive factors between COVID-19 patients requiring ICU admission for intubation/mechanical ventilation and all other COVID-19 cases in Selangor, Malaysia. METHOD: Field data collected during the COVID-19 outbreak in Selangor, Malaysia, up to 13 April 2020 were used, comprising socio-demographic characteristics, comorbidities and presenting symptoms of COVID-19 cases. ICU admission was determined from medical records. Multiple logistic regression analysis was performed to identify factors associated with ICU admission requiring intubation/mechanical ventilation among COVID-19 cases. RESULTS: A total of 1287 COVID-19-positive cases were included for analysis. The most common comorbidities were hypertension (15.5%) and diabetes (11.0%). More than one third of cases presented with fever (43.8%) or cough (37.1%). Of the 25 cases that required intubation/mechanical ventilation, 68.0% had hypertension, 88.0% had fever, 40.0% had dyspnoea and 44.0% were lethargic. Multivariate regression showed that cases that required intubation/mechanical ventilation had significantly higher odds of being older (aged 360 years) [adjusted odds ratio (aOR) = 3.9] and having hypertension (aOR = 5.7), fever (aOR = 9.8), dyspnoea (aOR = 9.6) or lethargy (aOR = 7.9) than cases that did not require intubation/mechanical ventilation. CONCLUSION: The COVID-19 cases in Selangor, Malaysia requiring intubation/mechanical ventilation were significantly older, with a higher proportion of hypertension and symptoms of fever, dyspnoea and lethargy. These risk factors have been reported previously for severe COVID-19 cases, and highlight the role that ageing and underlying comorbidities play in severe outcomes to respiratory disease.


Subject(s)
COVID-19/physiopathology , Comorbidity , Diabetes Complications , Hospitalization/statistics & numerical data , Hypertension/complications , Intensive Care Units/statistics & numerical data , Respiration, Artificial/statistics & numerical data , Severity of Illness Index , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/epidemiology , Female , Humans , Malaysia/epidemiology , Male , Middle Aged , Retrospective Studies , SARS-CoV-2
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